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This paper introduces information entropy as a general-purpose collective variable (CV) for enhanced sampling in molecular dynamics simulations, enabling the discovery of unanticipated transition mechanisms without prior knowledge of reaction coordinates. The method employs well-tempered metadynamics to bias simulations toward entropy-changing configurations, balancing exploration and thermodynamic sampling. The approach is validated across five diverse systems, demonstrating its ability to identify metastable states and reaction pathways, including those missed by conventional CVs.
Uncover hidden reaction pathways and metastable states in molecular simulations without any prior assumptions about the system's behavior.
Enhanced sampling methods typically require predefined collective variables (CVs) that presuppose knowledge of reaction coordinates, restricting the discovery of unanticipated transition mechanisms or intermediates. Here, we show that a local measure of information entropy in atomistic systems is a general-purpose CV for rare event sampling across molecular and condensed-phase systems. The method biases simulations toward entropy-changing configurations following a well-tempered metadynamics approach, thus balancing novelty and thermodynamic accessibility. Blind exploration of potential energy surfaces enables unsupervised discovery of metastable basins and reaction pathways, including competing transition channels inaccessible to conventional order parameters. We demonstrate the generality of the method across five systems spanning conformational sampling, homogeneous nucleation, glass formation, and solid-state phase transformations.